scholarly journals Optimization of C.I Engine Parameters Using Artificial Neural

2012 ◽  
pp. 175-181
Author(s):  
N. Balajiganesh ◽  
B.Chandra Mohan Reddy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modern Technology ◽  
Compression Ignition ◽  
Oil Blends ◽  
Compression Ignition Engines ◽  
Modern Process ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Experimental Values

Optimization of Compression Ignition Engines through advanced artificial neural network is the modern process in mechanization and best utilization of modern technology for better economic scenarios in coming generation. This project deals with the feasibility of using artificial neural networks in combination with genetic algorithms to optimize the diesel engine settings. The engine is operated by using diesel and sunflower oil blends and the output parameters are calculated theoretically with the standard mechanical formulae and those manual experimental calculated values are used for training several neural networks with different various hidden layer [ n x m ] matrix combinations. The output values given by these trained networks are compared with experimental values and out of which the trained error values are taken for all networks.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

The model of compression ignition engine with artificial neural networks

2008 ◽  
Vol 132 (1) ◽  
pp. 44-49
Author(s):  
Krzysztof BRZOZOWSKI ◽  
Jacek NOWAKOWSKI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Compression Ignition ◽  
Control Parameters ◽  
Working Process ◽  
Compression Ignition Engine ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Model Equations

The paper presents an application of artificial neural network in modelling the working process in compression ignition engine. In order to determine the usefulness of proposed method the optimisation task has been formulated. The aim of optimisation process was to find the engine control parameters which enable reduction of the NOx emission. In order to solve the problem, the model equations has to be integrated for values of control parameters whose are given as output from the neural networks implemented.

scholarly journals Use of Artificial Neural Network in Design of Fly Ash Blended Cement Concrete Mixes

2019 ◽  
Vol 8 (3) ◽  
pp. 4222-4233 ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fly Ash ◽  
Theoretical Calculations ◽  
Construction Material ◽  
Blended Cement ◽  
Sufficient Time ◽  
Cement Concrete ◽  
Artificial Neural ◽  
Experimental Values

Cement concrete is the most important construction material which is non-homogeneous in nature. Its strength depends on properties of its many constituent materials are diverse in nature. It is important to fix up exact proportions of these materials beforehand so that needed strength in concrete is obtained later on. Sufficient time is needed to check it by making trial mixes of concrete after fixing up the proportions by theoretical calculations and testing these trial mixes after 28 days. In this duration concreting work may be held up in the absence of a final approved mix in terms of quantities of various constituents of concrete. Use of artificial neural networks (ANNs) for the checking of design composition of fly ash blended cement concrete mixes which were designed as per Indian standard guidelines has been made. Prediction of strength of such mixes at a later date by ANN has also been explored in this study. Prediction results of ANNs come close to experimental values and reinforce the utility of ANNs in the area of use of civil engineering materials for improving efficiency in construction

scholarly journals Determining the Number of Neurons in Artificial Neural Networks for Approximation, Trained with Algorithms Using the Jacobi Matrix

TEM Journal ◽  
2020 ◽  
pp. 1320-1329
Author(s):  
Kostadin Yotov ◽  
Emil Hadzhikolev ◽  
Stanka Hadzhikoleva
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Jacobi Matrix ◽  
Optimal Number ◽  
Upper Limit ◽  
Artificial Neural ◽  
Hidden Layer

How can we determine the optimal number of neurons when constructing an artificial neural network? This is one of the most frequently asked questions when working with this type of artificial intelligence. Experience has brought the understanding that it takes an individual approach for each task to specify the number of neurons. Our method is based on the requirement of algorithms looking for a minimum of functions of type 𝑺􁈺𝒛􁈻 􀵌 Σ 􁈾𝝋𝒊 𝒎 􁈺𝒛 􁈻􁈿𝟐 𝒊􀭀𝟏 that satisfy the inequality 𝒑 􀵑 𝒎, where p is the dimensionality of the argument z, and m is the number of functions. Formulas for an upper limit of the required neurons are proposed for networks with one hidden layer and for networks with r hidden layers with an equal number of neurons.

scholarly journals PREDICTION OF THE SIZE OF NANOPARTICLES AND MICROSPORE SURFACE AREA USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 1 (1) ◽  
pp. 65
Author(s):  
Dženana Sarajlić ◽  
Layla Abdel-Ilah ◽  
Adnan Fojnica ◽  
Ahmed Osmanović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Surface Area ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Experimental Values

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

scholarly journals Jaringan Syaraf Tiruan Untuk Pengenalan Citra Sidik Jari Pada Smart Home Berbasis MYSQL

2018 ◽  
Vol 5 (5) ◽  
pp. 597
Author(s):  
Nur Yanti ◽  
Fathur Zaini Rachman ◽  
Nurwahidah Jamal ◽  
Era Purwanto ◽  
Fachrurozy Fachrurozy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Data Base ◽  
Monitoring System ◽  
Smart Home ◽  
Fingerprint Image ◽  
Artificial Neural ◽  
Hidden Layer

<p class="Abstrak"> </p><p class="Abstrak">Sistem keamanan yang bertujuan sebagai sistem monitoring pada <em>smart home</em> seperti memonitoring pengguna laboratorium, perpustakaan, atau ruangan penyimpanan dan peminjaman peralatan praktek di program studi suatu kampus, ruang penyimpanan senjata, hingga rumah tinggal, memerlukan sekuritas yang handal untuk memudahkan identifikasi pengguna ruangan atau pencegahan dari tindak pencurian, maka dirancang sistem monitoring melalui pengenalan citra sidik jari menggunakan sensor ZFM60, jaringan syaraf tiruan dan MySQL. Tujuannya agar di dapat pola yang relevan dari citra dan mengeliminasi informasi atau variabel yang tidak relevan. Metode yang digunakan yaitu <em>experimental</em>, terdiri dari pengumpulan data sidik jari, perancangan sistem pengolahan citra, pembuatan dan pengujian <em>hardware</em> dan <em>software</em>, serta implementasi sistem. Hasil proses pengenalan atau klarifikasi citra sidik jari melalui GUI Matlab, nilai <em>error</em> hasil pengolahan dan pelatihan citra sidik jari dengan jaringan syaraf tiruan, digunakan sebagai ciri citra dan disimpan sebagai <em>data base</em> pada MySQL, kemudian dibandingkan dengan nilai <em>error</em> citra sidik jari baru yang di klarifikasi. Nilai citra yang dapat dikenali berada diantara -0,0005 hingga 0,0005, diluar batas tersebut merupakan citra yang tidak dikenali. Selisih (nilai <em>error</em>) antara ciri citra yang tersimpan pada <em>data base</em> dan ciri citra yang diklarifikasi menghasilkan nilai <em>error </em>yang kecil yaitu &lt; 0.0005, menunjukkan jaringan syaraf tiruan <em>backpropagation</em> handal diimplementasikan pada pengenalan sidik jari untuk melatih pola citra dari sidik jari. Konfigurasi jaringan yaitu maksimal <em>epoch</em> = 3000, <em>learning rate</em> = 1, target <em>error</em> = 0.1, <em>hidden layer</em> = 17. Pelatihan jaringan syaraf tiruan pada konfigurasi tersebut menghasilkan nilai <em>error</em> terkecil dari ciri citra sebesar 0.0000085.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong><em> </em></p><p class="Judul2"><em><br /></em></p><p class="Judul2"><em>The security system that aims as a monitoring system in smart home such as monitoring laboratory users, libraries, or storage rooms and borrowing practical equipment in the study program of a campus, weapons storage room, to a residence, requires reliable securities to facilitate identification of room users or prevention from theft, it is designed a monitoring system through fingerprint image recognition using ZFM60 sensors, artificial neural networks and MySQL. The goal is to get relevant patterns from the image and eliminate irrelevant information or variables. The method used is experimental, consisting of fingerprint data collection, image processing system design, hardware and software manufacturing and testing, and system implementation. The result of the process of recognition or clarification of fingerprint images through the Matlab GUI, the error value of processing and training of fingerprint images with artificial neural networks, is used as a feature of the image and stored as a data base on MySQL, then compared with the error value of the new fingerprint image that is clarified. The recognizable image value is between -0,0005 to 0,0005, beyond this limit is an unrecognized image. The difference (error value) between the characteristics of the image stored in the data base and the clarified image feature produces a small error value of &lt;0.0005, indicating a reliable backpropagation artificial neural network is implemented in fingerprint recognition to train the image pattern of fingerprints. Network configuration is maximum epoch = 3000, learning rate = 1, target error = 0.1, hidden layer = 17. Artificial neural network training in the configuration produces the smallest error value of the image characteristics of 0.0000085.</em></p>

scholarly journals Plant Classification based on Leaves using Artificial Neural Network

2021 ◽  
Vol 13 (6) ◽  
Author(s):  
Vishwad Desai ◽  
◽  
Vijay Savani ◽  
Rutul Patel ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Computationally Efficient ◽  
Manual Methods ◽  
Computationally Expensive ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Manual methods to examine leaf for plant classification can be tedious, therefore, automation is desired. Existing methods try distinctive approaches to accomplish this task. Nowadays, Convolution Neural Networks (CNN) are widely used for such application which achieves higher accuracy. However, CNN's are computationally expensive and require extensive dataset for training. Other existing methods are far less resource expensive but they also have their shortcomings for example, some features cannot be processed accurately with automation, some necessary differentiators are left out. To overcome this, we have proposed a simple Artificial Neural Network (ANN) for automatic classification of plants based on their leaf features. Experimental results show that the proposed algorithm able to achieve an accuracy of 96% by incorporating only a single hidden layer of ANN. Hence, our approach is computationally efficient compared to existing CNN based methods.

The Influence of Changes in ANN Hidden Layer Unit and Feature Selection on Classification

2021 ◽  
Vol 9 (3) ◽  
pp. 351
Author(s):  
Sawendo Eko Wijana ◽  
I Gede Santi Astawa ◽  
AAIN Eka Karyawati
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Liver Disease ◽  
Multilayer Perceptron ◽  
Computation Time ◽  
Model Complex ◽  
Artificial Neural ◽  
Hidden Layer

Abstract Classification is the process of differentiating a set of models into several data classes. There are many methods that can be used for the classification process, one of which is the Artificial Neural Network method. Neural networks are a computational method that mimics biological syafar networks. Artificial condition networks can be used to model complex relationships between input and output to recognize patterns in data [1]. In this study, testing was conducted to determine the effect of uncorrelated or low-correlation features in the data classification process and the effect of changing the number of units in the hidden layer on the classification results. The data used in this study were liver disease dataobtained from the Kaggle Dataset.Where in comparing the results of using feature selection, it is divided into 4 predetermined scenarios through the search for significance values ??with the SPSS correlation test.In the results of the implementation of the Multilayer Perceptron which aims to determine the effect of feature selection on the classification results, the results are that feature selection does not really affect the computation time obtained, and correlated data has more influence on the accuracy obtained when compared to uncorrelated data. In the results of the implementation of the Multilayer Perceptron which aims to determine the effect of changing the number of hidden layer units on the classification results, the results show that changes in the number of units in the hidden layer in Artificial Neural Networks have increased significantly in accuracy in several scenarios, but the computation time increases if the number of units in the hidden layer increases. Keywords: Classification, Artificial Neural Network, Liver Disease, Accuracy, Time.

scholarly journals Artificial neural networks for solving elliptic differential equations with boundary layer

2020 ◽  
Author(s):  
Dongfang Yuan ◽  
Wenhui Liu ◽  
Yongbin Ge ◽  
Guimei Cui ◽  
Lin Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Convection Diffusion ◽  
Convection Diffusion Equation ◽  
Mapping Strategy ◽  
Elliptic Differential Equations ◽  
Artificial Neural ◽  
Hidden Layer

In this paper, we consider the artificial neural networks for solving the differential equation with boundary layer, in which the gradient of the solution changes sharply near the boundary layer. The solution of the boundary layer problems poses a huge challenge to both traditional numerical methods and artificial neural network methods. By theoretical analyzing the changing rate of the weights of first hidden layer near the boundary layer, a mapping strategy is added in traditional neural network to improve the convergence of the loss function. Numerical examples are carried out for the 1D and 2D convection-diffusion equation with boundary layer. The results demonstrate that the modified neural networks significantly improve the ability in approximating the solutions with sharp gradient.

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